Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries

Zheng, L; Zhu, J; Lu, DDC; Wang, G; He, T

Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries

Zheng, L Zhu, J

Lu, DDC

Wang, G

He, T

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Publication Type:: Journal Article
Citation:: Energy, 2018, 150 pp. 759 - 769
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Zheng, L	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.contributor.author	Lu, DDC https://orcid.org/0000-0003-2145-0580	en_US
dc.contributor.author	Wang, G https://orcid.org/0000-0003-4295-8578	en_US
dc.contributor.author	He, T	en_US
dc.date.available	2020-05-25T19:06:00Z
dc.date.issued	2018-05-01	en_US
dc.identifier.citation	Energy, 2018, 150 pp. 759 - 769	en_US
dc.identifier.issn	0360-5442	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130594
dc.description.abstract	© 2018 Elsevier Ltd The reliability and safety of battery operations necessitate an efficient battery management system (BMS) with accurate battery state of charge (SOC) and capacity estimation techniques. This paper investigates the incremental capacity analysis (ICA) and differential voltage analysis (DVA) methods for onboard battery SOC and capacity estimation. Since the conventional cell terminal voltage based ICA/DVA methods are sensitive to the changed battery resistance and polarization during battery aging processes, the SOC based ICA/DVA methods are proposed to address this problem as so to accurately identify features of interest on incremental capacity (IC) and differential voltage (DV) curves for applications. Three feature points (FPs) that are potential to be easily identified by battery management systems are extracted from the SOC based IC/DV curves, and then the relations between FPs and cell SOCs/capacities are quantified and applied for battery SOC and capacity estimation. The robustness of the proposed approach against various aging levels and erroneous cumulative capacities is evaluated. Promising results with the maximum absolute error of 1.0% and the relative error of 2.0% can be achieved for battery SOC and capacity estimation, respectively.	en_US
dc.relation.ispartof	Energy	en_US
dc.relation.isbasedon	10.1016/j.energy.2018.03.023	en_US
dc.subject.classification	Energy	en_US
dc.title	Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries	en_US
dc.type	Journal Article
utslib.citation.volume	150	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	0914 Resources Engineering and Extractive Metallurgy	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	150	en_US

Abstract:

© 2018 Elsevier Ltd The reliability and safety of battery operations necessitate an efficient battery management system (BMS) with accurate battery state of charge (SOC) and capacity estimation techniques. This paper investigates the incremental capacity analysis (ICA) and differential voltage analysis (DVA) methods for onboard battery SOC and capacity estimation. Since the conventional cell terminal voltage based ICA/DVA methods are sensitive to the changed battery resistance and polarization during battery aging processes, the SOC based ICA/DVA methods are proposed to address this problem as so to accurately identify features of interest on incremental capacity (IC) and differential voltage (DV) curves for applications. Three feature points (FPs) that are potential to be easily identified by battery management systems are extracted from the SOC based IC/DV curves, and then the relations between FPs and cell SOCs/capacities are quantified and applied for battery SOC and capacity estimation. The robustness of the proposed approach against various aging levels and erroneous cumulative capacities is evaluated. Promising results with the maximum absolute error of 1.0% and the relative error of 2.0% can be achieved for battery SOC and capacity estimation, respectively.

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http://hdl.handle.net/10453/130594